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SITUATION EXPLAINED: Why is China building a sovereign AI stack to compete with Nvidia? We asked Glinert 🇺🇸 🏭 and Mitchell Nahmias, co-founders of Sphere Semi. "What I see as the only real viable crack in Nvidia's armor is what's coming out of China, which is ironic given Jensen's...

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Ingraham: Trump announced he's lifting some export controls on Nvidia allowing the chip maker to sell some products to approved customers in China. Why are we giving anything to China? Johnson: I met with Jenson, the CEO of Nvidia and the way he explains is he has about 95% market share effectively of AI. And with the chips and everything that is involved in that, his argument is if he's not able to compete in China, their system can over take him and thus the U.S.
1:38

Ingraham: Trump announced he's lifting some export controls on Nvidia allowing the chip maker to sell some products to approved customers in China. Why are we giving anything to China? Johnson: I met with Jenson, the CEO of Nvidia and the way he explains is he has about 95% market share effectively of AI. And with the chips and everything that is involved in that, his argument is if he's not able to compete in China, their system can over take him and thus the U.S.

Acyn

133,472 Aufrufe • vor 8 Monaten

🚨🇺🇸🇨🇳 SPEAKER JOHNSON DEFENDS TRUMP SELLING NVIDIA AI CHIPS TO CHINA: "IF WE DON'T, THEY'LL OVERTAKE US" Speaker Johnson explained Trump's decision to lift export controls allowing Nvidia to sell AI chips to approved Chinese customers, warning that blocking sales could allow China to overtake America's AI dominance: "China is a principal threat except for the national debt. I met with Jensen, the CEO of Nvidia. He has about 95% market share of AI. His argument is if he's not able to compete in China, then their system can overtake him and thus overtake the US. It's a delicate balance. We've got to treat China as the adversary that they are but also they're a really important trade partner." Source: Fox
1:31

🚨🇺🇸🇨🇳 SPEAKER JOHNSON DEFENDS TRUMP SELLING NVIDIA AI CHIPS TO CHINA: "IF WE DON'T, THEY'LL OVERTAKE US" Speaker Johnson explained Trump's decision to lift export controls allowing Nvidia to sell AI chips to approved Chinese customers, warning that blocking sales could allow China to overtake America's AI dominance: "China is a principal threat except for the national debt. I met with Jensen, the CEO of Nvidia. He has about 95% market share of AI. His argument is if he's not able to compete in China, then their system can overtake him and thus overtake the US. It's a delicate balance. We've got to treat China as the adversary that they are but also they're a really important trade partner." Source: Fox

Mario Nawfal

28,949 Aufrufe • vor 8 Monaten

Chamath: "Nvidia is not doing what's in the best interest of the United States." 🇺🇸🇨🇳 "I think we can all do the math. About 47% of all of NVIDIA's revenue goes to China and Chinese-related countries." "And I think when you peel back this onion, what you will find is a whole raft of companies that were stood up to buy these Nvidia GPUs to essentially act as a waystation for China." "And I think that is the big problem." "Let's have a thought starter: if 47% of all of the AI capability and horsepower is being shipped to three Asian countries, where do you think the apps that require that amount of horsepower live?" "Is there a Cursor of Bhutan that we did not know? Is there a great shopping app in Cambodia that's come out of nowhere, that's AI powered?" "I think the answer is no." "Every single time we have an advance in the United States, how is it that Alibaba shows up with something incredible? DeepSeek shows up with something better?" "At every turn and at every step of AI, they are at the same rate or one step ahead." "To be honest with you, I think the real problem that we have is that Nvidia is not doing what is in the best interest of the United States." "You have a American company that has been working around the guidelines at every turn to try to land silicon into the hands of China." "Late last year, they introduced this thing called the H20 that was explicitly designed for China and to be compliant with US rules at the time." "Which again, gives these guys substantial performance." "This is a case where (Nvidia) has plausible deniability. I sell something to a Singaporean registered company? Plausible deniability." "What am I supposed to do? You can't expect me to audit it. I think that's what NVIDIA's answer will be to this question." "But what is the real expectation? At a minimum, the United States should have a mechanism to understand it." "It is implausible that if you did one or two layers of work, you would not find that most of this traffic is being used by Chinese organizations."
1:53

Chamath: "Nvidia is not doing what's in the best interest of the United States." 🇺🇸🇨🇳 "I think we can all do the math. About 47% of all of NVIDIA's revenue goes to China and Chinese-related countries." "And I think when you peel back this onion, what you will find is a whole raft of companies that were stood up to buy these Nvidia GPUs to essentially act as a waystation for China." "And I think that is the big problem." "Let's have a thought starter: if 47% of all of the AI capability and horsepower is being shipped to three Asian countries, where do you think the apps that require that amount of horsepower live?" "Is there a Cursor of Bhutan that we did not know? Is there a great shopping app in Cambodia that's come out of nowhere, that's AI powered?" "I think the answer is no." "Every single time we have an advance in the United States, how is it that Alibaba shows up with something incredible? DeepSeek shows up with something better?" "At every turn and at every step of AI, they are at the same rate or one step ahead." "To be honest with you, I think the real problem that we have is that Nvidia is not doing what is in the best interest of the United States." "You have a American company that has been working around the guidelines at every turn to try to land silicon into the hands of China." "Late last year, they introduced this thing called the H20 that was explicitly designed for China and to be compliant with US rules at the time." "Which again, gives these guys substantial performance." "This is a case where (Nvidia) has plausible deniability. I sell something to a Singaporean registered company? Plausible deniability." "What am I supposed to do? You can't expect me to audit it. I think that's what NVIDIA's answer will be to this question." "But what is the real expectation? At a minimum, the United States should have a mechanism to understand it." "It is implausible that if you did one or two layers of work, you would not find that most of this traffic is being used by Chinese organizations."

The All-In Podcast

910,450 Aufrufe • vor 1 Jahr

Zuck: "The export controls are clearly working in a way. DeepSeek did all these very impressive low-level optimizations. That is impressive. But then you ask: why did they have to do that when none of the American labs did? Well, because they're using partially nerfed chips—that are the only thing that Nvidia is allowed to sell in China because of the export controls."
1:16

Zuck: "The export controls are clearly working in a way. DeepSeek did all these very impressive low-level optimizations. That is impressive. But then you ask: why did they have to do that when none of the American labs did? Well, because they're using partially nerfed chips—that are the only thing that Nvidia is allowed to sell in China because of the export controls."

Dwarkesh Patel

361,585 Aufrufe • vor 1 Jahr

🚨 THIS IS EXTREMELY BAD FOR $NVDA. China is preparing a $295 billion plan to build AI data centers across the whole country. The catch for America: at least 80% of the chips must be homegrown. That means Huawei in. Nvidia out. Jensen Huang confirmed this last month. Nvidia has "largely conceded" China to Huawei, he told CNBC. China once accounted for at least 20% of Nvidia's entire data center revenue. That market is now all but GONE. America spent years locking China out. China just stopped knocking.
0:20

🚨 THIS IS EXTREMELY BAD FOR $NVDA. China is preparing a $295 billion plan to build AI data centers across the whole country. The catch for America: at least 80% of the chips must be homegrown. That means Huawei in. Nvidia out. Jensen Huang confirmed this last month. Nvidia has "largely conceded" China to Huawei, he told CNBC. China once accounted for at least 20% of Nvidia's entire data center revenue. That market is now all but GONE. America spent years locking China out. China just stopped knocking.

CryptoGoos

187,769 Aufrufe • vor 2 Monaten

Jensen Huang says it has “largely conceded” China’s AI chip market to Huawei Nvidia was not rejected because China “hates” Nvidia. It was pushed out because Washington turned chips into a leash. China learned the lesson very clearly: You cannot build AI sovereignty on hardware that your geopolitical rival can revoke with one export-control memo. Jensen Huang can be friendly. Nvidia can be excellent. Chinese demand can be huge. None of that changes the strategic reality. If the neck of China’s AI industry depends on an American-controlled chip supply, then it is not an industry. It is a hostage. So China did what any serious civilization-state would do: It built alternatives. It endured the pain. It let Huawei grow into the space Washington forced open. Now Nvidia says it has “largely conceded” China’s AI chip market to Huawei. That is not China closing a door. That is America sawing off the bridge, then acting surprised when China built its own road.
2:06

Jensen Huang says it has “largely conceded” China’s AI chip market to Huawei Nvidia was not rejected because China “hates” Nvidia. It was pushed out because Washington turned chips into a leash. China learned the lesson very clearly: You cannot build AI sovereignty on hardware that your geopolitical rival can revoke with one export-control memo. Jensen Huang can be friendly. Nvidia can be excellent. Chinese demand can be huge. None of that changes the strategic reality. If the neck of China’s AI industry depends on an American-controlled chip supply, then it is not an industry. It is a hostage. So China did what any serious civilization-state would do: It built alternatives. It endured the pain. It let Huawei grow into the space Washington forced open. Now Nvidia says it has “largely conceded” China’s AI chip market to Huawei. That is not China closing a door. That is America sawing off the bridge, then acting surprised when China built its own road.

𝘊𝘰𝘳𝘳𝘪𝘯𝘦

235,707 Aufrufe • vor 3 Monaten

🚨🚨🚨BREAKING: Nvidia CEO was just asked about his thoughts on Huawei and China > Huawei is one of the most formidable technology companies in the world. > they are incredible in computing, networking technology and software capabilities > they have all the essential capabilities to advance AI > China is right behind the US in AI > 50% of the world’s AI researchers are chinese The race is on.
1:20

🚨🚨🚨BREAKING: Nvidia CEO was just asked about his thoughts on Huawei and China > Huawei is one of the most formidable technology companies in the world. > they are incredible in computing, networking technology and software capabilities > they have all the essential capabilities to advance AI > China is right behind the US in AI > 50% of the world’s AI researchers are chinese The race is on.

NIK

585,511 Aufrufe • vor 1 Jahr

NVIDIA CEO: “CHINA IS MOVING INCREDIBLY FAST IN AI” Jensen Huang said China is accelerating AI on all fronts - infrastructure, models, and real-world applications: "China is fast to both invent a new technology, but also to integrate the technology, to apply the technology. And because there's so much competition in China, everybody wants to build a great company and wants to succeed, a lot of great capabilities come out. And at the foundation of all of that is China's education system has produced some of the world's finest AI researchers - about 50% of the world's AI researchers are based here in China." Source:
1:40

NVIDIA CEO: “CHINA IS MOVING INCREDIBLY FAST IN AI” Jensen Huang said China is accelerating AI on all fronts - infrastructure, models, and real-world applications: "China is fast to both invent a new technology, but also to integrate the technology, to apply the technology. And because there's so much competition in China, everybody wants to build a great company and wants to succeed, a lot of great capabilities come out. And at the foundation of all of that is China's education system has produced some of the world's finest AI researchers - about 50% of the world's AI researchers are based here in China." Source:

Mario Nawfal

1,018,768 Aufrufe • vor 1 Jahr

watched through the entire interview these guys aren't in the same league. jensen plays to win. dwarkesh has been one shotted by mythos marketing. in a way its jensen arguing against dario by proxy. think there's a fundamental underestimation that most ppl in the west have about huawei. they are a freak monster powerhouse and jensen is 100% correct in wanting to push nvidia in china versus letting huawei eat the domestic market. the worst outcome for the US would be for deepseek to be trained 100% on huawei chips versus a china that is dependent on the nvidia stack.
3:58

watched through the entire interview these guys aren't in the same league. jensen plays to win. dwarkesh has been one shotted by mythos marketing. in a way its jensen arguing against dario by proxy. think there's a fundamental underestimation that most ppl in the west have about huawei. they are a freak monster powerhouse and jensen is 100% correct in wanting to push nvidia in china versus letting huawei eat the domestic market. the worst outcome for the US would be for deepseek to be trained 100% on huawei chips versus a china that is dependent on the nvidia stack.

factorydoge

546,324 Aufrufe • vor 4 Monaten

Nvidia is pulling off the most sophisticated financial loop in tech history. They invested $40 BILLION in its own customers in just 5 months. Here's why this could blow up the entire AI economy: Nvidia generated $97 billion in free cash flow last year. Instead of sitting on it, Jensen started writing checks to every company in the AI supply chain. Not small checks. We're talking about billions at a time. And almost every single one of those companies turns around and spends that money on Nvidia chips. Follow the money: $30 billion into OpenAI. OpenAI is one of Nvidia's largest GPU customers and spends billions annually on Nvidia hardware through cloud providers. $2 billion into CoreWeave, a company that exists exclusively to rent out data centers full of Nvidia GPUs. $2 billion into Marvell for silicon photonics that connects Nvidia systems. $2 billion into Lumentum for optical tech that powers Nvidia data centers. $2 billion into Coherent for the same thing. $2 billion into Nebius, an AI cloud company deploying Nvidia infrastructure. $3.2 billion into Corning, the glassmaker building three new US factories specifically to make fiber optic cables for Nvidia's next-gen systems. $2.1 billion into IREN, a data center operator that just agreed to deploy 5 gigawatts of Nvidia-designed infrastructure. And the list goes on. Every single recipient either buys Nvidia chips directly, builds infrastructure that runs on Nvidia chips, or manufactures components that go inside Nvidia systems. Matthew Bryson, an analyst at Wedbush Securities, said in a research note that Nvidia's dealmaking fits "squarely into the circular investment theme." Bloomberg even published an entire interactive feature this week titled "AI Circular Deals: How Microsoft, OpenAI and Nvidia Keep Paying Each Other." The piece maps how capital flows between the same handful of companies and gets counted as revenue multiple times along the way. But here's the part that makes this genuinely complicated: Nvidia's $5 billion investment in Intel from September is now worth over $25 billion. That's a 5x return in months. Their private company portfolio went from $3.4 billion to $22.3 billion on the balance sheet in a single year. They booked $8.9 billion in gains from equity investments alone. So when critics say "circular investing," Nvidia can point to Intel and say "we turned $5 billion into $25 billion, this is just smart capital deployment." And they're not wrong. Some of these bets ARE paying off like crazy. The real question is whether Nvidia is a chipmaker that happens to invest, or a venture fund that happens to sell chips. Because right now Jensen is doing both at a scale that has never existed in the semiconductor industry. No chipmaker in history has EVER invested $40 billion in its own ecosystem in five months. Last fiscal year Nvidia invested $17.5 billion in private companies. Their SEC filing literally says those investments include "AI model companies that purchase its products directly or through cloud service providers." They're saying it themselves: We invest in companies that buy our products. On Nvidia's last earnings call, Jensen told investors their investments are focused on "expanding and deepening our ecosystem reach." Translate that from CEO-speak and it means " we're funding the companies that fund us. The bull case says Nvidia is building an unbreakable moat by financing the entire AI supply chain and ensuring it all runs on Nvidia hardware. The bear case says this is the most elaborate circular revenue scheme since the subprime mortgage era and it all breaks apart the moment one domino falls. Both cases use the exact same evidence.
1:01

Nvidia is pulling off the most sophisticated financial loop in tech history. They invested $40 BILLION in its own customers in just 5 months. Here's why this could blow up the entire AI economy: Nvidia generated $97 billion in free cash flow last year. Instead of sitting on it, Jensen started writing checks to every company in the AI supply chain. Not small checks. We're talking about billions at a time. And almost every single one of those companies turns around and spends that money on Nvidia chips. Follow the money: $30 billion into OpenAI. OpenAI is one of Nvidia's largest GPU customers and spends billions annually on Nvidia hardware through cloud providers. $2 billion into CoreWeave, a company that exists exclusively to rent out data centers full of Nvidia GPUs. $2 billion into Marvell for silicon photonics that connects Nvidia systems. $2 billion into Lumentum for optical tech that powers Nvidia data centers. $2 billion into Coherent for the same thing. $2 billion into Nebius, an AI cloud company deploying Nvidia infrastructure. $3.2 billion into Corning, the glassmaker building three new US factories specifically to make fiber optic cables for Nvidia's next-gen systems. $2.1 billion into IREN, a data center operator that just agreed to deploy 5 gigawatts of Nvidia-designed infrastructure. And the list goes on. Every single recipient either buys Nvidia chips directly, builds infrastructure that runs on Nvidia chips, or manufactures components that go inside Nvidia systems. Matthew Bryson, an analyst at Wedbush Securities, said in a research note that Nvidia's dealmaking fits "squarely into the circular investment theme." Bloomberg even published an entire interactive feature this week titled "AI Circular Deals: How Microsoft, OpenAI and Nvidia Keep Paying Each Other." The piece maps how capital flows between the same handful of companies and gets counted as revenue multiple times along the way. But here's the part that makes this genuinely complicated: Nvidia's $5 billion investment in Intel from September is now worth over $25 billion. That's a 5x return in months. Their private company portfolio went from $3.4 billion to $22.3 billion on the balance sheet in a single year. They booked $8.9 billion in gains from equity investments alone. So when critics say "circular investing," Nvidia can point to Intel and say "we turned $5 billion into $25 billion, this is just smart capital deployment." And they're not wrong. Some of these bets ARE paying off like crazy. The real question is whether Nvidia is a chipmaker that happens to invest, or a venture fund that happens to sell chips. Because right now Jensen is doing both at a scale that has never existed in the semiconductor industry. No chipmaker in history has EVER invested $40 billion in its own ecosystem in five months. Last fiscal year Nvidia invested $17.5 billion in private companies. Their SEC filing literally says those investments include "AI model companies that purchase its products directly or through cloud service providers." They're saying it themselves: We invest in companies that buy our products. On Nvidia's last earnings call, Jensen told investors their investments are focused on "expanding and deepening our ecosystem reach." Translate that from CEO-speak and it means " we're funding the companies that fund us. The bull case says Nvidia is building an unbreakable moat by financing the entire AI supply chain and ensuring it all runs on Nvidia hardware. The bear case says this is the most elaborate circular revenue scheme since the subprime mortgage era and it all breaks apart the moment one domino falls. Both cases use the exact same evidence.

Ricardo

159,345 Aufrufe • vor 3 Monaten

We asked (Co-Founder & CEO, webAI) about the advantages of running AI on devices. "There's no free lunch, but there are things cloud players can't do. It's RAM intensive, yes, but by distributing across devices, we can unlock more accuracy and context. But if you're hosting for a million users on Nvidia, you can't do that." "With our stack, everything's downstream only. Think about health data, like you want that to be private. The dream is personalized models for millions that never leave their device."
1:19

We asked (Co-Founder & CEO, webAI) about the advantages of running AI on devices. "There's no free lunch, but there are things cloud players can't do. It's RAM intensive, yes, but by distributing across devices, we can unlock more accuracy and context. But if you're hosting for a million users on Nvidia, you can't do that." "With our stack, everything's downstream only. Think about health data, like you want that to be private. The dream is personalized models for millions that never leave their device."

TBPN

10,109 Aufrufe • vor 1 Jahr

Jensen Huang just admitted the biggest AI labs can't borrow money like normal companies. So Nvidia signs for them, and they spend it on Nvidia chips. Nvidia reported Wednesday and the numbers are absurd: Revenue of $96.2 billion, up 106%, with net income of $59.7 billion, the most profitable quarter any public company has EVER posted. And Huang just told Fox Business that every chip Nvidia can make next year is already sold. Here's why this matters the most: Huang wrote this himself about his own customers: "Frontier AI labs have extraordinary demand for training and inference compute, but many are growing faster than their balance sheets and long-term credit profiles can support." Then: They "still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently." Put simply: His customers can't get the loans. So Nvidia signs for them. There's a compute campus going up in Ohio with OpenAI as the tenant. Nvidia has tied roughly $105 billion in commitments to it. OpenAI's existing and planned commitments now come to about 12 gigawatts of Nvidia compute. CFO Colette Kress told analysts Nvidia will also provide selective credit enhancement for nearly 2 gigawatts of compute at a second frontier lab. She wouldn't say which one. Nvidia put up to $10 billion into Anthropic in November at a valuation near $350 billion, and Anthropic agreed to buy up to a gigawatt of Grace Blackwell and Vera Rubin systems in the same deal. And Nvidia isn't only guaranteeing these companies. It OWNS pieces of them. This week's filing shows $18 billion committed to equity investments for the rest of the fiscal year, and $47.9 billion already sitting in private companies as of late July. Now here's where it gets really insane: Last week, Huang sat on a CNBC set surrounded by six of Wall Street's biggest firms. Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR. They signed a memorandum to mobilise up to $500 billion in outside capital for AI data centres. Nvidia kept the option to backstop up to a quarter of those deals. And Huang used that stage to announce that Nvidia GPUs are now an asset class. Pension and credit funds can now lend against graphics cards the way they lend against office towers. Kress saw the accusation coming and got ahead of it on the earnings call: "We recognise the scale of this support, and we know some will call this circular financing. We see it differently." But look at the two things Huang says about the same companies. On the earnings call he said AI has hit its inflection point, that the tokens are productive and profitable, and that compute is now revenue. But he also said those same labs can't secure investment-grade financing on their own. A business that's inflecting into profit is exactly the business a bank lends to. Banks lend against cash flow every day. But Nvidia‘s guarantee exists because something in that first story isn't landing with the people whose job is pricing risk. Kress does have a real answer to this though. She said the second lab's credit support only complements capacity it already secured on its own, without Nvidia backing it. Vendor financing is also old and legal. Cisco did it and GE built a finance arm on it. Huang's case is that Nvidia understands these businesses better than any lender could, and he says the risk is low and his only regret is not investing more and sooner. He may be completely right. But one thing is certain: Nvidia guarantees the paper. The paper buys the chips. Nvidia books the sale. Then Nvidia tells you the order book is full for a year. That order book is the entire argument for a $5 trillion company. And Jensen Huang just explained, in his own words, that his customers couldn't have written those orders without him. Isn’t this suspicious?
1:35

Jensen Huang just admitted the biggest AI labs can't borrow money like normal companies. So Nvidia signs for them, and they spend it on Nvidia chips. Nvidia reported Wednesday and the numbers are absurd: Revenue of $96.2 billion, up 106%, with net income of $59.7 billion, the most profitable quarter any public company has EVER posted. And Huang just told Fox Business that every chip Nvidia can make next year is already sold. Here's why this matters the most: Huang wrote this himself about his own customers: "Frontier AI labs have extraordinary demand for training and inference compute, but many are growing faster than their balance sheets and long-term credit profiles can support." Then: They "still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently." Put simply: His customers can't get the loans. So Nvidia signs for them. There's a compute campus going up in Ohio with OpenAI as the tenant. Nvidia has tied roughly $105 billion in commitments to it. OpenAI's existing and planned commitments now come to about 12 gigawatts of Nvidia compute. CFO Colette Kress told analysts Nvidia will also provide selective credit enhancement for nearly 2 gigawatts of compute at a second frontier lab. She wouldn't say which one. Nvidia put up to $10 billion into Anthropic in November at a valuation near $350 billion, and Anthropic agreed to buy up to a gigawatt of Grace Blackwell and Vera Rubin systems in the same deal. And Nvidia isn't only guaranteeing these companies. It OWNS pieces of them. This week's filing shows $18 billion committed to equity investments for the rest of the fiscal year, and $47.9 billion already sitting in private companies as of late July. Now here's where it gets really insane: Last week, Huang sat on a CNBC set surrounded by six of Wall Street's biggest firms. Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR. They signed a memorandum to mobilise up to $500 billion in outside capital for AI data centres. Nvidia kept the option to backstop up to a quarter of those deals. And Huang used that stage to announce that Nvidia GPUs are now an asset class. Pension and credit funds can now lend against graphics cards the way they lend against office towers. Kress saw the accusation coming and got ahead of it on the earnings call: "We recognise the scale of this support, and we know some will call this circular financing. We see it differently." But look at the two things Huang says about the same companies. On the earnings call he said AI has hit its inflection point, that the tokens are productive and profitable, and that compute is now revenue. But he also said those same labs can't secure investment-grade financing on their own. A business that's inflecting into profit is exactly the business a bank lends to. Banks lend against cash flow every day. But Nvidia‘s guarantee exists because something in that first story isn't landing with the people whose job is pricing risk. Kress does have a real answer to this though. She said the second lab's credit support only complements capacity it already secured on its own, without Nvidia backing it. Vendor financing is also old and legal. Cisco did it and GE built a finance arm on it. Huang's case is that Nvidia understands these businesses better than any lender could, and he says the risk is low and his only regret is not investing more and sooner. He may be completely right. But one thing is certain: Nvidia guarantees the paper. The paper buys the chips. Nvidia books the sale. Then Nvidia tells you the order book is full for a year. That order book is the entire argument for a $5 trillion company. And Jensen Huang just explained, in his own words, that his customers couldn't have written those orders without him. Isn’t this suspicious?

Ricardo

54,580 Aufrufe • vor 1 Tag

Earlier this year we announced that telecom leaders are building AI grids using NVIDIA AI infrastructure to optimize inference on distributed networks But what actually is an AI Grid? In this video, Amogh Dendukuri takes us back to basics. Watch now to see him break down the top 5 things you need to know about AI grids.
2:23

Earlier this year we announced that telecom leaders are building AI grids using NVIDIA AI infrastructure to optimize inference on distributed networks But what actually is an AI Grid? In this video, Amogh Dendukuri takes us back to basics. Watch now to see him break down the top 5 things you need to know about AI grids.

NVIDIA

27,166 Aufrufe • vor 2 Monaten

THAT MOMENT WHEN NVIDIA CEO JENSEN HUANG WAS ASKED ABOUT HIS COMMENTS ON CHINA BEATING THE U.S. IN THE AI RACE: “THAT’S NOT WHAT I SAID. WHAT I SAID WAS THAT CHINA HAS VERY GOOD AI TECHNOLOGY. THEY HAVE MANY AI RESEARCHERS. IN FACT, 50% OF THE WORLD’S AI RESEARCHERS ARE IN CHINA. THEY DEVELOP VERY GOOD AI TECHNOLOGY. THE MOST POPULAR OPEN-SOURCE AI MODELS IN THE WORLD TODAY ARE FROM CHINA. SO THEY ARE MOVING VERY, VERY FAST. THE UNITED STATES HAS TO CONTINUE TO MOVE INCREDIBLY FAST. OTHERWISE, THE WORLD IS VERY COMPETITIVE, SO WE HAVE TO RUN FAST.”
0:40

THAT MOMENT WHEN NVIDIA CEO JENSEN HUANG WAS ASKED ABOUT HIS COMMENTS ON CHINA BEATING THE U.S. IN THE AI RACE: “THAT’S NOT WHAT I SAID. WHAT I SAID WAS THAT CHINA HAS VERY GOOD AI TECHNOLOGY. THEY HAVE MANY AI RESEARCHERS. IN FACT, 50% OF THE WORLD’S AI RESEARCHERS ARE IN CHINA. THEY DEVELOP VERY GOOD AI TECHNOLOGY. THE MOST POPULAR OPEN-SOURCE AI MODELS IN THE WORLD TODAY ARE FROM CHINA. SO THEY ARE MOVING VERY, VERY FAST. THE UNITED STATES HAS TO CONTINUE TO MOVE INCREDIBLY FAST. OTHERWISE, THE WORLD IS VERY COMPETITIVE, SO WE HAVE TO RUN FAST.”

Vivek Sen

155,599 Aufrufe • vor 2 Monaten

Ben Thompson explains how LLMs greatly diminished Nvidia's CUDA moat even as they sent the stock to the moon "So the weird thing about large language models is they were obviously incredible for Nvidia. That's why their stock went to the moon." "They have been on and off the most valuable company in the world." "It was also very bad for Nvidia. And the reason it was bad for Nvidia is that the play with CUDA is to build a developer ecosystem on top of CUDA." "But CUDA only works on Nvidia GPUs. So you get CUDA for free. It's easier to use, and it's a tremendous investment. Nvidia almost went under trying to build CUDA at a time when no one understood what they were doing or why they were wasting money on it." "And that's why Jensen Huang will get bristly, particularly when people question their rent-seeking or profit, whatever. It's like, no, they earned their spot fair and square." "Absolutely. It shouldn't be forgotten. They have earned every dollar they've gotten through 25 years of taking massive risks." "It bottomed out in October 2022. I wrote an article like three weeks before ChatGPT came out, tracing their bottoming-out history and their search for what was next." "'Nvidia in the Valley.' So, go back to this GTC. So I wrote an article at the time called 'Nvidia Waves and Moats'." "And what was interesting about that GTC was, number one, it was very boring. All the cool stuff kind of got scrubbed out." "Now, Jensen Huang has brought that stuff back, so the last few GTCs he's more talking about other things. Now it comes across as, oh, you're still looking for something beyond the LLM." "Because the problem with the LLM is it shifts the developer platform far above where Nvidia sits. All the activity is happening on top of LLMs. And so no one who's writing an AI application today is using CUDA." "Now, some people are, if you're training your own model and you're doing some low-level things or non-LLM things." "But the vast majority of the energy and all the money and the ecosystem is far removed from CUDA." "They have no idea and don't need to know or care what chips their application is running on. They're just on the OpenAI API, or the Anthropic API, or using Bedrock on Amazon, and it's sitting on Trainium, and they're using a Chinese open-source model. It's totally abstracted away, and this is why LLMs were bad for Nvidia." "Now, again, all the money they made along the way is worth it, but their moat has been tremendously diminished." "CUDA is still a moat if you need to do stuff that requires CUDA. But the vast majority of stuff, in energy, doesn't require CUDA, like in a post-LLM world."
3:04

Ben Thompson explains how LLMs greatly diminished Nvidia's CUDA moat even as they sent the stock to the moon "So the weird thing about large language models is they were obviously incredible for Nvidia. That's why their stock went to the moon." "They have been on and off the most valuable company in the world." "It was also very bad for Nvidia. And the reason it was bad for Nvidia is that the play with CUDA is to build a developer ecosystem on top of CUDA." "But CUDA only works on Nvidia GPUs. So you get CUDA for free. It's easier to use, and it's a tremendous investment. Nvidia almost went under trying to build CUDA at a time when no one understood what they were doing or why they were wasting money on it." "And that's why Jensen Huang will get bristly, particularly when people question their rent-seeking or profit, whatever. It's like, no, they earned their spot fair and square." "Absolutely. It shouldn't be forgotten. They have earned every dollar they've gotten through 25 years of taking massive risks." "It bottomed out in October 2022. I wrote an article like three weeks before ChatGPT came out, tracing their bottoming-out history and their search for what was next." "'Nvidia in the Valley.' So, go back to this GTC. So I wrote an article at the time called 'Nvidia Waves and Moats'." "And what was interesting about that GTC was, number one, it was very boring. All the cool stuff kind of got scrubbed out." "Now, Jensen Huang has brought that stuff back, so the last few GTCs he's more talking about other things. Now it comes across as, oh, you're still looking for something beyond the LLM." "Because the problem with the LLM is it shifts the developer platform far above where Nvidia sits. All the activity is happening on top of LLMs. And so no one who's writing an AI application today is using CUDA." "Now, some people are, if you're training your own model and you're doing some low-level things or non-LLM things." "But the vast majority of the energy and all the money and the ecosystem is far removed from CUDA." "They have no idea and don't need to know or care what chips their application is running on. They're just on the OpenAI API, or the Anthropic API, or using Bedrock on Amazon, and it's sitting on Trainium, and they're using a Chinese open-source model. It's totally abstracted away, and this is why LLMs were bad for Nvidia." "Now, again, all the money they made along the way is worth it, but their moat has been tremendously diminished." "CUDA is still a moat if you need to do stuff that requires CUDA. But the vast majority of stuff, in energy, doesn't require CUDA, like in a post-LLM world."

Fireside Alpha

12,980 Aufrufe • vor 7 Tagen

Elon Musk on where people should invest: "I don't really buy stocks. I don't look for things to invest in. I just try to build things and then there happens to be stock of the company that I built, but I don't have like a portfolio or anything; Google is gonna be pretty valuable in the future. They've laid the ground work for an immense amount of value creation from an AI standpoint. Nvidia is obvious at this point. There's an argument that companies that do AI and robotics, and maybe spaceflight, are going to be overwhelmingly almost all of the value."
1:29

Elon Musk on where people should invest: "I don't really buy stocks. I don't look for things to invest in. I just try to build things and then there happens to be stock of the company that I built, but I don't have like a portfolio or anything; Google is gonna be pretty valuable in the future. They've laid the ground work for an immense amount of value creation from an AI standpoint. Nvidia is obvious at this point. There's an argument that companies that do AI and robotics, and maybe spaceflight, are going to be overwhelmingly almost all of the value."

Sawyer Merritt

4,473,186 Aufrufe • vor 9 Monaten

AI investment is expanding beyond models and applications into the full stack that supports them. Energy capacity, semiconductor supply, compute infrastructure, and software layers are becoming more connected, influencing how and where AI can be built and deployed. In partnership with NVIDIA, The Futurum Group examines the Five Layers of the AI Cake framework as a way to understand the structure of the AI economy. The market brief outlines how value is distributed across the stack, where bottlenecks are forming, and what these dynamics mean for enterprise strategy, capital allocation, workforce planning, and global competitiveness. ⏯️ Explore the key findings, and use the link to download the full report: 👉 #AIInfrastructure #NVIDIA #Futurum
1:12

AI investment is expanding beyond models and applications into the full stack that supports them. Energy capacity, semiconductor supply, compute infrastructure, and software layers are becoming more connected, influencing how and where AI can be built and deployed. In partnership with NVIDIA, The Futurum Group examines the Five Layers of the AI Cake framework as a way to understand the structure of the AI economy. The market brief outlines how value is distributed across the stack, where bottlenecks are forming, and what these dynamics mean for enterprise strategy, capital allocation, workforce planning, and global competitiveness. ⏯️ Explore the key findings, and use the link to download the full report: 👉 #AIInfrastructure #NVIDIA #Futurum

The Futurum Group

75,859 Aufrufe • vor 3 Monaten

The AI Cold War: USA🇺🇸 vs China🇨🇳 On E221, the besties discussed how US efforts to impede China’s AI progress by restricting GPU exports are producing unintended consequences. Chamath Palihapitiya: "The US government is really trying to make sure that these next-gen GPUs don't go directly to China, but also don't go to countries that could then redirect them into China." Gavin Baker: " We're putting enough friction into the system that it does theoretically give America an advantage at the cost of creating tremendous incentives for China to develop their own semiconductor ecosystem and ... you know, necessity is the mother of innovation." " These export controls are creating an immense incentive for China to be really algorithmically innovative." "And you saw that with DeepSeek where there were some real algorithmic innovations." @jason: " Said another way, if we squeeze too tightly on letting them buy the Nvidia chips, they might make a better Nvidia." Chamath: "They're trying now." Jason: "What are the chances they succeed in your mind, Gavin?" Gavin: " In the next five years, I think zero. I think it's really, really hard." "But over ten years, who knows?"
0:55

The AI Cold War: USA🇺🇸 vs China🇨🇳 On E221, the besties discussed how US efforts to impede China’s AI progress by restricting GPU exports are producing unintended consequences. Chamath Palihapitiya: "The US government is really trying to make sure that these next-gen GPUs don't go directly to China, but also don't go to countries that could then redirect them into China." Gavin Baker: " We're putting enough friction into the system that it does theoretically give America an advantage at the cost of creating tremendous incentives for China to develop their own semiconductor ecosystem and ... you know, necessity is the mother of innovation." " These export controls are creating an immense incentive for China to be really algorithmically innovative." "And you saw that with DeepSeek where there were some real algorithmic innovations." @jason: " Said another way, if we squeeze too tightly on letting them buy the Nvidia chips, they might make a better Nvidia." Chamath: "They're trying now." Jason: "What are the chances they succeed in your mind, Gavin?" Gavin: " In the next five years, I think zero. I think it's really, really hard." "But over ten years, who knows?"

The All-In Podcast

49,734 Aufrufe • vor 1 Jahr

This is the first stage. We are now in a situation where it's 10 and 10 plus 20 for fentanyl, so 30% tariffs in the US of China goods coming in, and they can get rid of 20% of it if they solve the fentanyl problem. That they will do with certainty. But we’re not finished there and neither are millions of small entrepreneurs in America that I represent. We need IP law to be finally put into place so that our products and services are protected in their markets the way we protect theirs and ours. So, reciprocal playing field is where we need to go. This is a great first step but it's only the beginning. I want to sell to the Chinese people. I have no problem with them. I have a big problem with their government. They don't abide by the rules. We've known that for a long time and no admin has dealt with China, even after they came into the World Trade Organization in 2000. They've breached every covenant of that agreement so it's important for us to set the tone in terms of how we're going to go forward with China.
2:50

This is the first stage. We are now in a situation where it's 10 and 10 plus 20 for fentanyl, so 30% tariffs in the US of China goods coming in, and they can get rid of 20% of it if they solve the fentanyl problem. That they will do with certainty. But we’re not finished there and neither are millions of small entrepreneurs in America that I represent. We need IP law to be finally put into place so that our products and services are protected in their markets the way we protect theirs and ours. So, reciprocal playing field is where we need to go. This is a great first step but it's only the beginning. I want to sell to the Chinese people. I have no problem with them. I have a big problem with their government. They don't abide by the rules. We've known that for a long time and no admin has dealt with China, even after they came into the World Trade Organization in 2000. They've breached every covenant of that agreement so it's important for us to set the tone in terms of how we're going to go forward with China.

Kevin O'Leary aka Mr. Wonderful

50,199 Aufrufe • vor 1 Jahr